The notions of "sample" & "population" are
irrelevant when dealing inferentially with correlation.

True
False

Researchers usually build CIs around their sample values of r rather
than deal with null hypotheses.

True
False

Statistical Tests Involving a Single Correlation Coefficient

Do researchers very often make inferences based upon a single correlation
computed from a single sample?

Yes
No

The "direction" of the researcher's inference moves from
the population to the sample.

True
False

What pinpoint number is usually in the null hypothesis?

-1.00
-.50
0.00
+.50
+1.00

In his/her research article, does the typical researcher indicate,
either in words or symbols, the null hypothesis that he/she tested?

Yes
No

Look at Excerpt 9.5. Express in symbols the null hypothesis that
most likely was tested . . . and rejected.

Ho: r = -1.00
Ho: r = 0.00
Ho: r = +1.00
Ho: r = 0.76
Ho: r = 0.05

The sample value of r, computed from the collected data, typically
serves as the calculated value.

True
False

If r is compared against a critical value, Ho will be
rejected if the former is __ than the latter.

Larger
Smaller

If p is compared against the level of significance, Ho will
be rejected if the former is __ than the latter.

Larger
Smaller

Can tests on correlation coefficients be conducted in a one-tailed
fashion?

Yes
No

Inferential tests can be done on Pearson & Spearman correlations
but not on biserial or point-biserial correlations.

True
False

If a researcher tests a correlation coefficient but does not
indicate the type of correlation, you should guess that it was
Pearson's r.

True
False

Tests on Many Correlation Coefficients (Each of Which is Treated Separately)

Do researchers very often set up and test more than 1 correlational
null hypothesis in the same study?

Yes
No

If two or more rs are tested in the same study, the null hypothesis
will likely be the same in all tests.

True
False

Look at Excerpt 9.12. If there had been 8 coping strategies rather than
6, how many null hypotheses would have been tested in this
excerpt's Table?

8
15
16
28
64

If a researcher computes all possible bivariate correlations among
5 variables, what would the Bonferroni-corrected alpha level be if
the researcher wants to keep Type I error risk at 5% for the set
of tests being conducted?

.25
.05
.01
.005

Tests on Reliability and Validity Coefficients

Can a researcher set up and test a null hypothesis concerning a
reliability or validity coefficient?

Yes
No

Look at Excerpt 9.16. In order for the GMAT to have accounted for 80% of the variability
among the final MBA GPAs, how high would the correlation have needed to be?

.894 (i.e., the square root of .80)
.64 (i.e., the square of .80)

Statistically Comparing Two Correlation C oefficients

If a researcher inferentially compares two correlations, there might
be 1 group involved or 2 groups.

True
False

If the correlation between height and weight for a sample of men
is compared with the correlation between height and weight in a sample
of women, how many inferences would be made to the 2 populations?

1
2
3
4

The Use of Confidence Intervals Around Correlation Coefficients

Which is more popular: setting up and testing a correlational Hoor building a CI around the sample value of r?

Setting up and testing a correlational Ho
Building a CI around the sample value of r

If a researcher discovered that r = .13 and that CI.95 = .06 to
.20, would he/she claim p < .05?

Yes
No

Cautions

Can a researcher's r turn out to be close to zero and yet still
be significantly different from zero?

Yes
No

Which of these would constitute better evidence that there is, in
the population, a strong relationship between the two variables
that a researcher has measured and found to be significantly related:

p < .0001
r2 = .70
n = 10,000

Do many researchers concern themselves with the notions of "power"
and "effect size" when testing their correlations?

Yes
No

If a correlation coefficient is found to be statistically significant,
and if a power analysis is then conducted, is it fair to assume that
a "strong" relationship exists if the statistical test is shown to
have had "high" power?

Yes
No

Do many folks concerns themselves with the notions of "linearity"
& "homoscedasticity" when testing their correlations?

Yes
No

Which of these terms is a fairly good synonym for the term "homoscedasticity?"

Equal means
Equal variances
Equal correlations
Equal variables

If a bivariate correlation coefficient has been found to be statistically
significant with p<.05 (or better yet, with p<.01), the researcher
can legitimately infer that a causal relationship exists between the
2 variables.

True
False

Attenuation causes r to ____ r, the
magnitude of the correlation in the population.

underestimate
overestimate

What causes attenuation?

An n that's too small
Measurement errors
Inadequate statistical power

Who is connected to the procedure that goes by the name "r-to-z transformation"?

Pearson
Spearman
Cohen
Fisher

These Questions are Supposed to be a Bit More Challenging

Look at Excerpt 9.18. If the value of the second r had
turned out equal to .26 (rather than equal to -.22), how many of the
3 results would have been significant?

All 3 of them
2 of them
1 of them
None of them

It's a good guess to think that the study from which Excerpt 9.3
involved a ___ (small/large) sample size.

small
large

In Excerpt 9.4, an r of .13 turned out to be nonsignificant. In
Excerpt 9.12, an r of .14 turned
out to be significant (with p < .001). Assuming that both of these
rs were of the Pearson product-moment variety, how could the results be so
different when the sample correlation coefficients are almost identical?

The null hypotheses were different in the two studies.
The alternative hypotheses were different in the two studies.
The sample sizes were different in the two studies.